DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty

Etienne Goffinet; Sen Yan; Fabrizio Gabellieri; Laurence Jennings; Lydia Gkoura; Filippo Castiglione; Ryan Young; Idir Malki; Ankita Singh; Thomas Launey

arXiv · 2502.05230

DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty

Etienne Goffinet·Sen Yan·Fabrizio Gabellieri·Laurence Jennings·Lydia Gkoura·Filippo Castiglione·Ryan Young·Idir Malki·Ankita Singh·Thomas Launey

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Abstract

Nuclear Magnetic Resonance (NMR) spectrometry uses electro-frequency pulses to probe the resonance of a compound's nucleus, which is then analyzed to determine its structure. The acquisition time of high-resolution NMR spectra remains a significant bottleneck, especially for complex biological samples such as proteins. In this study, we propose a novel and efficient sub-sampling strategy based on a diffusion model trained on protein NMR data. Our method iteratively reconstructs under-sampled spectra while using model uncertainty to guide subsequent sampling, significantly reducing acquisition time. Compared to state-of-the-art strategies, our approach improves reconstruction accuracy by 52.9\%, reduces hallucinated peaks by 55.6%, and requires 60% less time in complex NMR experiments. This advancement holds promise for many applications, from drug discovery to materials science, where rapid and high-resolution spectral analysis is critical.

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BibTeX RIS

Etienne Goffinet, Sen Yan, Fabrizio Gabellieri, Laurence Jennings, Lydia Gkoura, Filippo Castiglione, Ryan Young, Idir Malki, Ankita Singh, Thomas Launey. 2025-02-06. DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty. https://arxiv.org/abs/2502.05230

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